bar plots

code/bar_plot.py

@@ -0,0 +1,139 @@
+import os
+
+from experiment import Experiment
+# noinspection PyUnresolvedReferences
+from soup import Soup
+from typing import List
+
+from collections import defaultdict
+
+from argparse import ArgumentParser
+import numpy as np
+
+import plotly as pl
+import plotly.graph_objs as go
+
+import colorlover as cl
+
+import dill
+
+
+def build_args():
+    arg_parser = ArgumentParser()
+    arg_parser.add_argument('-i', '--in_file', nargs=1, type=str)
+    arg_parser.add_argument('-o', '--out_file', nargs='?', default='out', type=str)
+    return arg_parser.parse_args()
+
+
+def plot_histogram(bars_dict_list: List[dict], filename='histogram_plot'):
+    # catagorical
+    ryb = cl.scales['10']['div']['RdYlBu']
+
+    data = []
+
+    if bars_dict_list:
+        keys = bars_dict_list[0].keys()
+        keyDict = defaultdict(list)
+    else:
+        raise IOError('This List is empty, is this intended?')
+
+    for key in keys:
+        keyDict[key] = np.mean([bars_dict[key] for bars_dict in bars_dict_list])
+
+    hist = go.Bar(
+        y=[keyDict.get(key, 0) for key in keys],
+        x=[key for key in keys],
+        showlegend=False,
+        marker=dict(
+            color=[ryb[bar_id] for bar_id in range(len(keys))]
+        ),
+    )
+    data.append(hist)
+
+    layout = dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
+                  # height=400, width=400,
+                  # margin=dict(l=20, r=20, t=20, b=20)
+                  )
+
+    fig = go.Figure(data=data, layout=layout)
+    pl.offline.plot(fig, auto_open=True, filename=filename)
+    pass
+
+
+def line_plot(line_dict_list, filename='lineplot'):
+    # lines with standard deviation
+    # Transform data accordingly and plot it
+    data = []
+    rdylgn = cl.scales['10']['div']['RdYlGn']
+    rdylgn_background = [scale + (0.4,) for scale in cl.to_numeric(rdylgn)]
+    for line_id, line_dict in enumerate(line_dict_list):
+        name = line_dict.get('name', 'gimme a name')
+
+        upper_bound = go.Scatter(
+            name='Upper Bound',
+            x=line_dict['x'],
+            y=line_dict['upper_y'],
+            mode='lines',
+            marker=dict(color="#444"),
+            line=dict(width=0),
+            fillcolor=rdylgn_background[line_id],
+        )
+
+        trace = go.Scatter(
+            x=line_dict['x'],
+            y=line_dict['main_y'],
+            mode='lines',
+            name=name,
+            line=dict(color=line_id),
+            fillcolor=rdylgn_background[line_id],
+            fill='tonexty')
+
+        lower_bound = go.Scatter(
+            name='Lower Bound',
+            x=line_dict['x'],
+            y=line_dict['lower_y'],
+            marker=dict(color="#444"),
+            line=dict(width=0),
+            mode='lines')
+
+        data.extend([upper_bound, trace, lower_bound])
+
+    layout=dict(title='{} Line Plot'.format('Experiment Name Penis'),
+                height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
+
+    fig = go.Figure(data=data, layout=layout)
+    pl.offline.plot(fig, auto_open=True, filename=filename)
+    pass
+
+
+def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_for=[]):
+    if os.path.isdir(absolut_file_or_folder):
+        for sub_file_or_folder in os.scandir(absolut_file_or_folder):
+            search_and_apply(sub_file_or_folder.path, plotting_function, files_to_look_for=files_to_look_for)
+    elif absolut_file_or_folder.endswith('.dill'):
+        file_or_folder = os.path.split(absolut_file_or_folder)[-1]
+        if file_or_folder in files_to_look_for and not os.path.exists('{}.html'.format(file_or_folder[:-5])):
+            print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__,
+                                                                             file=absolut_file_or_folder)
+                  )
+
+            with open(absolut_file_or_folder, 'rb') as in_f:
+                exp = dill.load(in_f)
+
+            plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5]))
+
+        else:
+            pass
+            # This was not a file i should look for.
+    else:
+        # This was either another FilyType or Plot.html alerady exists.
+        pass
+
+
+if __name__ == '__main__':
+    args = build_args()
+    in_file = args.in_file[0]
+    out_file = args.out_file
+
+    search_and_apply(in_file, plot_histogram, files_to_look_for=['all_counters.dill'])
+    # , 'all_names.dill', 'all_notable_nets.dill'])

code/setups/fixpoint-density.py

@@ -1,6 +1,7 @@
 import sys
-
+import os
-sys.path += ['../', './']
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
 
 from util import *
 from experiment import *
@@ -26,7 +27,7 @@ def count(counters, net, notable_nets=[]):
     return counters, notable_nets
 
 with Experiment('fixpoint-density') as exp:
-    exp.trials = 1000
+    exp.trials = 100
     exp.epsilon = 1e-4
     net_generators = []
     for activation in ['linear', 'sigmoid', 'relu']:
@@ -43,6 +44,7 @@ with Experiment('fixpoint-density') as exp:
             net = net_generator().with_params(epsilon=exp.epsilon)
             name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
             count(counters, net, notable_nets)
+            K.clear_session()
         all_counters += [counters]
         all_notable_nets += [notable_nets]
         all_names += [name]
@@ -52,4 +54,6 @@ with Experiment('fixpoint-density') as exp:
     for exp_id, counter in enumerate(all_counters):
         exp.log(all_names[exp_id])
         exp.log(all_counters[exp_id])
-        exp.log('\n')
+        exp.log('\n')
+
+print('Done')

code/setups/known-fixpoint-variation.py

@@ -1,6 +1,10 @@
 import sys
 
-sys.path += ['../', './']
+import os
+
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
 
 from util import *
 from experiment import *

code/setups/mixed-self-fixpoints.py

@@ -1,6 +1,7 @@
 import sys
+import os
 
-sys.path += ['../', './']
+from typing import Tuple
 
 from util import *
 from experiment import *
@@ -9,10 +10,32 @@ from network import *
 import keras.backend
 
 
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
+
 def generate_counters():
+    """
+    Initial build of the counter dict, to store counts.
+
+    :rtype: dict
+    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
+    """
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
+
 def count(counters, net, notable_nets=[]):
+    """
+    Count the occurences ot the types of weight trajectories.
+
+    :param counters:      A counter dictionary.
+    :param net:           A Neural Network
+    :param notable_nets:  A list to store and save intersting candidates
+
+    :rtype      Tuple[dict, list]
+    :return:    Both the counter dictionary and the list of interessting nets.
+    """
+
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():
@@ -28,6 +51,7 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
+
 with Experiment('training_fixpoint') as exp:
     exp.trials = 20
     exp.selfattacks = 4
@@ -69,4 +93,4 @@ with Experiment('training_fixpoint') as exp:
     for exp_id, name in enumerate(all_names):
         exp.log(all_names[exp_id])
         exp.log(all_data[exp_id])
-        exp.log('\n')
+        exp.log('\n')

code/setups/training-fixpoints.py

@@ -1,6 +1,8 @@
 import sys
+import os
 
-sys.path += ['../', './']
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
 
 from util import *
 from experiment import *